Non-destructive models for leaf area estimation in chickpea cultivars (<i>Cicer arietinum</i> L.)

Puntel, Simone; Schwab, Natália Teixeira; Pes, Luciano Zucuni; Alchieri, Douglas Vicente; Gatto, Ricardo Bianchi; Trachta, Mariano Abel

doi:10.1590/0034-737X202370050018

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Agricultural Engineering • Rev. Ceres 70 (5) • 2023 • https://doi.org/10.1590/0034-737X202370050018 copy

Non-destructive models for leaf area estimation in chickpea cultivars (Cicer arietinum L.)¹ 1 This article is an integral part of the undergraduate thesis of Simone Puntel, who graduated with a degree in Agronomy from the Universidade Federal de Santa Maria, in 2021. The work was supported by resources from the ‘Edital Conjunto de Circulação Interna 2020/2021’ from the Universidade Federal de Santa Maria.

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Chickpea is a winter legume with prospects for worldwide consumption growth, mainly demanded by Asian countries. Thus, studies on its growth, such as the leaf area (LA), are important to determine proper management practices for its cultivation. The objective of this work was to determine a mathematical model able to estimate the leaf area (LA) of chickpea in a non-destructive method. For this, a field experiment was carried out in the county of Santa Maria (RS), with five cultivars available in the national market. Several leaves of each cultivar were collected, and their greatest width (W) and longest length (L) were measured. With the aid of a scanner, these leaves were photocopied, and their LA (cm²) was determined using software. With the power model, models relating to LA and its dimensions were determined. These models were tested using various statistics, with independent data. The results indicate that for the cultivars BRS Aleppo, BRS Kalifa, Jamu, and BRS Toro the best model is LA= 0,0940.(L)^1.8483 and for the cultivar BRS Cícero the best model is LA = 0,1092.L^2.1815.

Keywords
pulses; linear dimensions; mathematical models

		Statistical Data^* * MAE = mean absolute error (cm2/leaf); RMSE = root mean square error (cm2/leaf); BIAS = BIAS index; d = agreement index; d1 = modified agreement index.
Model	Cultivar	MAE	RMSE	BIAS	D	d1
LA = 0,3237.(W)^0,2080	BRS Aleppo	0.4284	0.5620	-0.1054	0.8963	0.7180
LA = 0,0783.(L)^1,9314	BRS Aleppo	0.2431	0.3287	-0.0480	0.9682	0.8446
LA = 0,1435.(W.L)^1,0578	BRS Aleppo	0.2812	0.3709	-0.0762	0.9573	0.8161
LA = 0,3485.(W)^2,1913	BRS Kalifa	0.3280	0.4473	-0.0816	0.9487	0.7962
LA = 0,1101.(L)^1,7830	BRS Kalifa	0.1957	0.2692	-0.0352	0.9829	0.8811
LA = 0,1706.(W.L)^1,0224	BRS Kalifa	0.1913	0.2651	-0.0503	0.9832	0.8832
LA = 0,2910.(W)^1,8718	Jamu	0.2152	0.2934	-0.0514	0.9708	0.8377
LA = 0,1065.(L)^1,7733	Jamu	0.1604	0.2232	-0.0207	0.9848	0.8840
LA = 0,1619.(W.L)^0,9501	Jamu	0.1157	0.1527	-0.0252	0.9929	0.9146
LA = 0,4964.(W)^1,3907	BRS Cícero	0.4222	0.5738	0.0072	0.8568	0.6800
LA = 0,1092.(L)^2,1815	BRS Cícero	0.2591	0.3863	-0.0333	0.9432	0.8090
LA = 0,2420.(W.L)^0,9272	BRS Cícero	0.3162	0.4352	0.0074	0.9257	0.7685
LA = 0,2909.(W)^2,2003	BRS Toro	0.4534	0.6572	-0.1115	0.9363	0.7976
LA = 0,0924.(L)^1,8521	BRS Toro	0.2844	0.4382	-0.0827	0.9743	0.8771
LA = 0,1372.(W.L)^1,0626	BRS Toro	0.2780	0.4056	-0.0874	0.9779	0.8796

		Statistical Data^* * MAE = mean absolute error (cm2/leaf); RMSE = root mean square error (cm2/leaf); BIAS = BIAS index; d = agreement index; d1 = modified agreement index. The values in red and green represent the worst and the best results, respectively, of the statistics for each one of the cultivars.
Model	Cultivar	MAE	RMSE	BIAS	D	d1
LA= 0,3269.(W)^2,1552	BRS Aleppo	0.4432	0.5763	-0.1323	0.8871	0.7042
LA = 0,0940.(L)^1,8482	BRS Aleppo	0.2348	0.3168	-0.0031	0.9707	0.8504
LA = 0,1527.(W.L)^1,0380	BRS Aleppo	0.2734	0.3634	-0.0625	0.9590	0.8208
LA = 0,3269.(W)^2,1552	BRS Kalifa	0.3795	0.5281	-0.1622	0.9233	0.7609
LA = 0,0940.(L)^1,8482	BRS Kalifa	0.2144	0.3033	-0.0836	0.9779	0.8693
LA = 0,1527.(W.L)^1,0380	BRS Kalifa	0.2386	0.3384	-0.1175	0.9714	0.8526
LA = 0,3269.(W)^2,1552	Jamu	0.4323	0.5857	0.3562	0.9327	0.7341
LA = 0,0940.(.)^1,8482	Jamu	0.1552	0.2161	-0.0335	0.9861	0.8893
LA = 0,1527.(W.L)^1,0380	Jamu	0.1769	0.2339	0.1298	0.9869	0.8808
LA = 0,3269.(W)^2,1552	BRS Toro	0.4259	0.6108	-0.0419	0.9474	0.8127
LA = 0,0940.(L)^1,8482	BRS Toro	0.2790	0.4286	-0.0732	0.9756	0.8797
LA = 0,1527.(W.L)^1,0380	BRS Toro	0.2578	0.3706	-0.0484	0.9819	0.8889

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[1] *Corresponding author: simonepuntel16@gmail.com